{"id":"https://openalex.org/W4385767669","doi":"https://doi.org/10.24963/ijcai.2023/202","title":"Dichotomous Image Segmentation with Frequency Priors","display_name":"Dichotomous Image Segmentation with Frequency Priors","publication_year":2023,"publication_date":"2023-08-01","ids":{"openalex":"https://openalex.org/W4385767669","doi":"https://doi.org/10.24963/ijcai.2023/202"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2023/202","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2023/202","pdf_url":"https://www.ijcai.org/proceedings/2023/0202.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2023/0202.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100680326","display_name":"Yan Zhou","orcid":"https://orcid.org/0009-0003-0886-5906"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]},{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Zhou","raw_affiliation_strings":["Northwestern Polytechnical University","Zhejiang University","Northwestern Polytechnical University; Zhejiang University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University","institution_ids":["https://openalex.org/I17145004"]},{"raw_affiliation_string":"Zhejiang University","institution_ids":["https://openalex.org/I76130692"]},{"raw_affiliation_string":"Northwestern Polytechnical University; Zhejiang University","institution_ids":["https://openalex.org/I17145004","https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059740579","display_name":"Bo Dong","orcid":"https://orcid.org/0000-0002-6456-5452"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Dong","raw_affiliation_strings":["Zhejiang University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang University","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112014078","display_name":"Yuanfeng Wu","orcid":"https://orcid.org/0000-0001-8427-9851"},"institutions":[{"id":"https://openalex.org/I4210123185","display_name":"Zhejiang Lab","ror":"https://ror.org/02m2h7991","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210123185"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanfeng Wu","raw_affiliation_strings":["Zhejiang Lab"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang Lab","institution_ids":["https://openalex.org/I4210123185"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031854562","display_name":"Wentao Zhu","orcid":"https://orcid.org/0000-0001-9290-1778"},"institutions":[{"id":"https://openalex.org/I4210123185","display_name":"Zhejiang Lab","ror":"https://ror.org/02m2h7991","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210123185"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wentao Zhu","raw_affiliation_strings":["Zhejiang Lab"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang Lab","institution_ids":["https://openalex.org/I4210123185"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100765638","display_name":"Geng Chen","orcid":"https://orcid.org/0000-0001-8350-6581"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Geng Chen","raw_affiliation_strings":["Northwestern Polytechnical University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028235866","display_name":"Yanning Zhang","orcid":"https://orcid.org/0000-0002-2977-8057"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanning Zhang","raw_affiliation_strings":["Northwestern Polytechnical University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Northwestern Polytechnical University","institution_ids":["https://openalex.org/I17145004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.6993,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.89771617,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1822","last_page":"1830"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.9042797684669495},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7202291488647461},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.639445424079895},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6201845407485962},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5420970916748047},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5318424701690674},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.5277543663978577},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.515507698059082},{"id":"https://openalex.org/keywords/frequency-domain","display_name":"Frequency domain","score":0.48336929082870483},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.47647616267204285},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4483793377876282},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.44208860397338867},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4176560044288635},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4135187864303589},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.30956584215164185},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.28041332960128784},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.2017870843410492}],"concepts":[{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.9042797684669495},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7202291488647461},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.639445424079895},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6201845407485962},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5420970916748047},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5318424701690674},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.5277543663978577},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.515507698059082},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.48336929082870483},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.47647616267204285},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4483793377876282},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.44208860397338867},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4176560044288635},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4135187864303589},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.30956584215164185},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28041332960128784},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2017870843410492},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2023/202","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2023/202","pdf_url":"https://www.ijcai.org/proceedings/2023/0202.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2023/202","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2023/202","pdf_url":"https://www.ijcai.org/proceedings/2023/0202.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.41999998688697815}],"awards":[{"id":"https://openalex.org/G1049482041","display_name":null,"funder_award_id":"62001425","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5997687662","display_name":null,"funder_award_id":"D5000220213","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G7973357982","display_name":null,"funder_award_id":"62201465","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4385767669.pdf"},"referenced_works_count":47,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1983586459","https://openalex.org/W1994922096","https://openalex.org/W2168676389","https://openalex.org/W2194775991","https://openalex.org/W2515585988","https://openalex.org/W2560023338","https://openalex.org/W2773031116","https://openalex.org/W2775714787","https://openalex.org/W2776107444","https://openalex.org/W2884555738","https://openalex.org/W2884585870","https://openalex.org/W2961348656","https://openalex.org/W2963112696","https://openalex.org/W2963529609","https://openalex.org/W2963868681","https://openalex.org/W2964309882","https://openalex.org/W2987809008","https://openalex.org/W2997316506","https://openalex.org/W2998449272","https://openalex.org/W3014641072","https://openalex.org/W3021248054","https://openalex.org/W3025800305","https://openalex.org/W3092344722","https://openalex.org/W3094728142","https://openalex.org/W3125478936","https://openalex.org/W3164098653","https://openalex.org/W3169865585","https://openalex.org/W3173782971","https://openalex.org/W3179443972","https://openalex.org/W3182849571","https://openalex.org/W3189960127","https://openalex.org/W4210692941","https://openalex.org/W4213078714","https://openalex.org/W4239072543","https://openalex.org/W4285310685","https://openalex.org/W4286508603","https://openalex.org/W4308456711","https://openalex.org/W4312258849","https://openalex.org/W4312964941","https://openalex.org/W4313023779","https://openalex.org/W4313160444","https://openalex.org/W4382240924","https://openalex.org/W4382468778","https://openalex.org/W4382877880","https://openalex.org/W4385976148","https://openalex.org/W4386057725"],"related_works":["https://openalex.org/W2580650124","https://openalex.org/W4386190339","https://openalex.org/W2968424575","https://openalex.org/W3142333283","https://openalex.org/W3122088529","https://openalex.org/W3041320102","https://openalex.org/W2111669074","https://openalex.org/W2085259108","https://openalex.org/W3123087812","https://openalex.org/W2063076820"],"abstract_inverted_index":{"Dichotomous":[0],"image":[1],"segmentation":[2],"(DIS)":[3],"has":[4],"a":[5,58,65,107,118,151],"wide":[6],"range":[7],"of":[8,156],"real-world":[9],"applications":[10],"and":[11,68],"gained":[12],"increasing":[13],"research":[14],"attention":[15],"in":[16,42,154],"recent":[17],"years.":[18],"In":[19],"this":[20],"paper,":[21],"we":[22,56,84,116],"propose":[23,57,117],"to":[24,50,62,71,91,110,123],"tackle":[25],"DIS":[26],"with":[27],"informative":[28,73],"frequency":[29,44,59,74,79,119,126],"priors.":[30,75],"Our":[31],"model,":[32],"called":[33],"FP-DIS,":[34],"stems":[35],"from":[36],"the":[37,43,78,82,87,112,125,139],"fact":[38],"that":[39,144],"prior":[40,60,120],"knowledge":[41],"domain":[45],"can":[46],"provide":[47],"valuable":[48],"cues":[49],"identify":[51],"fine-grained":[52],"object":[53],"boundaries.":[54],"Specifically,":[55],"generator":[61],"jointly":[63],"utilize":[64],"fixed":[66],"filter":[67],"learnable":[69],"filters":[70],"extract":[72],"Before":[76],"embedding":[77,121],"priors":[80,127],"into":[81,128],"network,":[83],"first":[85],"harmonize":[86,111],"multi-scale":[88,129],"side-out":[89],"features":[90,130],"reduce":[92],"their":[93],"heterogeneity.":[94],"This":[95],"is":[96,104],"achieved":[97],"by":[98,150],"our":[99,145],"feature":[100],"harmonization":[101],"module,":[102],"which":[103],"based":[105],"on":[106,138],"gating":[108],"mechanism":[109],"grouped":[113],"features.":[114],"Finally,":[115],"module":[122],"embed":[124],"through":[131],"an":[132],"adaptive":[133],"modulation":[134],"strategy.":[135],"Extensive":[136],"experiments":[137],"benchmark":[140],"dataset,":[141],"DIS5K,":[142],"demonstrate":[143],"FP-DIS":[146],"outperforms":[147],"state-of-the-art":[148],"methods":[149],"large":[152],"margin":[153],"terms":[155],"key":[157],"evaluation":[158],"metrics.":[159]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":5}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}